Airlines have miles loyalty programs, in which customers are classified into a number of tiers according to their usage of the Airlines, and are awarded if they are in a higher tier and are more frequent users of the program. For example, a miles program may use three main tiers: basic, silver and gold, to classify customers by value, ordered from less to more important users.
By using historical data of customers, it is possible to predict future events, such as whether in forthcoming month(s), a new passenger would become a privileged frequent flyer. This information can then be used in Custom Relationship Management (CRM) interactions between the airline and the passenger and resource allocation of the airline system.
Various methods have been proposed and used for optimizing operations in the airline industry. In 2003, a model that predicts no-show ratio with the purpose of optimizing overbooking practice claimed to increase revenues by 0.4% to 3.2%. More recently, in 2013, Alaska Airlines in cooperation with G.E and Kaggle Inc., launched a $250,000 prize competition with the aim of optimizing costs by modifying flight plans. However, there is very little work on miles programs or frequent flier programs that focus on enhancing their value. In 2001 work focused on segmenting users according to return flight and length of stays was carried out.
Current methods for predicting future tier status of a customer also include linear extrapolation. FIG. 1 shows how linear extrapolation is used to predict when or if a customer will become high-value customer. A passenger flight activity is observed on a monthly basis, then a linear extrapolation is made for projecting how much they will fly in the coming month(s).